{
 "cells": [
  {
   "cell_type": "code",
   "id": "8191ccb0-e438-4ce4-9372-4c6ebc5c80d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:39:27.447239Z",
     "start_time": "2025-08-29T10:39:26.771790Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:42:37.029389Z",
     "start_time": "2025-08-29T10:42:36.996626Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#读取数据\n",
    "advertising = pd.read_csv('../../data/advertising.csv')\n",
    "print(advertising.head())#前五条数据\n",
    "print(advertising.describe())#数据的描述\n",
    "\"\"\"\n",
    "行\n",
    "count：非缺失值的数量。这里所有列的 count 都是 200，说明整个数据集没有缺失值，共 200 条记录。\n",
    "mean：均值，即该列所有数值的平均值，反映数据的集中趋势。\n",
    "std：标准差，衡量数据的离散程度（数据相对于均值的分散情况，标准差越大，数据越分散）。\n",
    "min：该列的最小值。\n",
    "25%：下四分位数（第一四分位数），表示有 25% 的数据小于该值，75% 的数据大于该值。\n",
    "50%：中位数（第二四分位数），将数据从小到大排列后，位于中间位置的数值，反映数据的中间水平。\n",
    "75%：上四分位数（第三四分位数），表示有 75% 的数据小于该值，25% 的数据大于该值。\n",
    "max：该列的最大值。\n",
    "列（数据字段）\n",
    "Unnamed: 0：通常是数据的索引列（比如行号），从 1 到 200，对应 200 条记录。\n",
    "TV、Radio、Newspaper：是广告投放相关的特征（比如 TV 广告花费、广播广告花费、报纸广告花费等）。\n",
    "Sales：销售额（目标变量，即要预测或分析的结果）\n",
    "\"\"\"\n",
    "print(advertising.shape)#数据的规格，目前是公200条数据，包含5列"
   ],
   "id": "3e8d5513d84bad3c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0     TV  Radio  Newspaper  Sales\n",
      "0           1  230.1   37.8       69.2   22.1\n",
      "1           2   44.5   39.3       45.1   10.4\n",
      "2           3   17.2   45.9       69.3    9.3\n",
      "3           4  151.5   41.3       58.5   18.5\n",
      "4           5  180.8   10.8       58.4   12.9\n",
      "       Unnamed: 0          TV       Radio   Newspaper       Sales\n",
      "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
      "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
      "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
      "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
      "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
      "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
      "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
      "max    200.000000  296.400000   49.600000  114.000000   27.000000\n",
      "(200, 5)\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:49:13.197495Z",
     "start_time": "2025-08-29T10:49:13.188751Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#数据预处理  \n",
    "#第一列是没有用的，去除第一列的ID\n",
    "advertising.drop(advertising.columns[0], axis=1, inplace=True)\n",
    "\"\"\"\n",
    "advertising.columns[0]代表数据的第一列\n",
    "axis=0代表行 1代表列；\n",
    "inplace：可选，布尔值，默认值为False。如果设置为True，则在原DataFrame上直接进行删除操作，函数返回None；如果设置为False，则返回一个新的DataFrame，原DataFrame保持不变 \n",
    "\"\"\"\n",
    "#去掉控制\n",
    "advertising.dropna(inplace=True)\n",
    "#提取特征和标签\n",
    "X = advertising.drop(columns='Sales')\n",
    "\"\"\"\n",
    "最后一列是目标值，去除掉最后一列别的都是特征值\n",
    "\"\"\"\n",
    "Y = advertising[\"Sales\"]"
   ],
   "id": "44849dff5616683f",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:49:15.639970Z",
     "start_time": "2025-08-29T10:49:15.633445Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(X.shape)#剩下的只有3列\n",
    "print(Y.shape)#只有1列"
   ],
   "id": "156404ff54e5c263",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 3)\n",
      "(200,)\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:51:07.831354Z",
     "start_time": "2025-08-29T10:51:07.645040Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#计算皮尔逊相关系数\n",
    "print(X.corrwith(Y, method=\"pearson\"))#代表与Y计算相关系数，并且使用的的方法是皮尔逊相关系数方法"
   ],
   "id": "11fc0626d7f91455",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TV           0.782224\n",
      "Radio        0.576223\n",
      "Newspaper    0.228299\n",
      "dtype: float64\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:57:08.440853Z",
     "start_time": "2025-08-29T10:57:08.428733Z"
    }
   },
   "cell_type": "code",
   "source": [
    "corr_matrix = advertising.corr(method=\"pearson\")\n",
    "print(corr_matrix)#计算自己内部的相关系数,和sales相关性越强代表这种方式效果越好"
   ],
   "id": "a2f6e870ba5e07a3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 TV     Radio  Newspaper     Sales\n",
      "TV         1.000000  0.054809   0.056648  0.782224\n",
      "Radio      0.054809  1.000000   0.354104  0.576223\n",
      "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
      "Sales      0.782224  0.576223   0.228299  1.000000\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T10:59:02.025177Z",
     "start_time": "2025-08-29T10:59:01.377684Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#将相关系数矩阵画成热力图\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ],
   "id": "3242495eac988bfc",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-29T11:00:10.899235Z",
     "start_time": "2025-08-29T11:00:10.735078Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sns.heatmap(corr_matrix, annot=True,cmap=\"coolwarm\", fmt=\".2f\")\n",
    "plt.title(\"Feature Correlation Matrix\")\n",
    "plt.show()"
   ],
   "id": "b8ef5ca92ecc2a4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "c2f0af9bb6185830"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
